Large language models are rapidly transforming medical education, yet their performance in Allergy/Immunology remains insufficiently characterized. Furthermore, concerns regarding accuracy, consistency, and sensitivity to input format persist.
Author(s): Carroll, Moshe, Kentis, Sabrina, Kareff, Hannah, Schechter, Clyde, Jariwala, Sunit
DOI: 10.1055/a-2946-7393
Human Factors (HF) principles are essential for safe and effective clinical decision support (CDS), yet existing guidance is fragmented and rarely evaluated in real world settings. A novel, evidence-based, vendor-agnostic HF-informed guideline was developed to address this gap. This study evaluated its perceived usefulness, usability, and impact on CDS design and optimization.
Author(s): Awad, Selvana, Loveday, Thomas, Baillie, Andrew, Baysari, Melissa T
DOI: 10.1093/jamia/ocag138
Evaluation of ambient AI on patient experience, documentation efficiency, clinician workload, and clinical throughput across a large emergency department (ED) network.
Author(s): Kashiouris, Markos G, Miner, Andrew, Saleh, Sameh, Scripps, Matthrew, Stanton, Lindsey, Samuel, Golda, Dibble, Brent
DOI: 10.1055/a-2939-3038
Ambient clinical documentation tools are increasingly used to reduce administrative burden and improve provider experience. However, evidence describing their effects at scale across large, multi-site health systems remains limited.
Author(s): Svetly, Andrew, Razzouk, Elie, McLean, Mary, Frye, Kenneth, Little, Andrew, Massaro, Lana, Quailey, Cheyenne, Svetly, Conner, Patel, Aatish, Montiel, Jesus
DOI: 10.1055/a-2936-7516